I'm a Research Scientist at Apple. I work on making machine learning more useful and
easier to use. I'm motivated by the following observation:

Over the last two decades, the machine learning community has made amazing progress.
Computers can recognize speech, diagnose cancer, and drive cars. Computers can do things
that we could barely imagine years ago. So why aren’t there more products that use
machine learning? Why do we need machine learning PhDs to train successful models? Why
is building software powered by machine learning so hard? I think about these questions,
search for answers, and in the process, end up creating software that makes it easier for
more people to use machine learning.

I have a Ph.D. in computer science from the University of Washington. At UW, I studied
machine learning engineers, understood their workflows, and built tools to make their jobs
easier. Before UW, I was at Stanford and Carnegie Mellon where I built self-driving cars,
studied how people learn in virtual reality, and built and deployed wearable speech
recognition systems.

After UW, I started and led the Colaboratory
team as part of Google Research. While at Google, I helped create and teach Introduction to
Data Science at Columbia University as well as two large-scale internal courses on data science
and machine learning.

At Apple, I split my time between building better machine learning developer tools and helping
design new products that use machine learning.